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Hyperspectral imaging provides the ability to collect spatially and spectrally resolved absorption spectra of organic films to gauge crystallinity, doping efficiency, material composition, thickness and uniformity.

Defect management strategies are vital for enhancing the performance of perovskite-based optoelectronic devices, such as perovskite-based light-emitting diodes (PeLEDs). As additives can fucntion both as acrystallization modifier and/or defect passivator, a thorough study on the roles of additives is essential, especially for blue emissive Pe-LEDs, where the emission is strictly controlled by the n-domain distribution of the Ruddlesden–Popper (RP, L2An–1PbnX3n+1, where L refers to a bulky cation, while A and X are monovalent cation, and halide anion, respectively) perovskite films. Of the various additives that are available, octyl phosphonic acid (OPA) is of immense interest because of its ability to bind with uncoordinated Pb2+ ( notorious for nonradiative recombination) and therefore passivates them. Here, with the help of various spectroscopic techniques, such as X-ray photon-spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), and photoluminescence quantum yield (PLQY) measurements, we demonstrate the capability of OPA to bind and passivate unpaired Pb2+ defect sites. Modification to crystallization promoting higher n-domain formation is also observed from steady-state and transient absorption (TA) measurements. With OPA treatment, both the PLQY and EQE of the corresponding PeLED showed improvements up to 53% and 3.7% at peak emission wavelength of 485 nm, respectively. Ministry of Education (MOE) National Research Foundation (NRF) Submitted/Accepted version This research was funded by National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Competitive Research Programme (CRP Award NRFCRP14-2014-03) and Ministry of Education, Singapore (MOE2019-T2-2-097). The photophysics studies were supported by the grants funded by the Singapore Ministry of Education under its AcRF Tier 2 grant (MOE-T2EP50120- 0004) and the NRF under NRF Investigatorship (NRF-NRFI2018-04).

Acknowledgements: The authors acknowledge funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043. K. H. also acknowledges funding from the NRF Fellowship NRF-NRFF13-2021-0011. The authors thank Alexei Lapkin for insightful discussions about the work., In this work, we develop a proxy high-throughput viscometer for Newtonian fluids with viscosities between 1500 and 12 000 cP.

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural to wonder what lessons can be learned from other fields undergoing similar developments. In this Review, we comparatively assess the evolution of applied ML in materials research, gameplaying and robotics. We observe ML being integrated into each field in three phases: first into discrete hardware and software tools (toolset integration); second across different steps in a workflow (workflow integration); and third through the incorporation, generation and representation of generalizable knowledge beyond any one study (knowledge integration). We identify transferrable lessons from gameplaying and robotics to materials research, including adaptive and accessible automation, the gamification of grand challenges to focus community efforts on specific workflow integrations and motivate benchmarks and canonical datasets, and the adoption of hybrid (data-based and model-based) algorithms that combine domain expertise and current learning to economically address high-complexity tasks. We identify opportunities for researchers from different fields to collaborate, including novel ways to represent and integrate a rich but heterogeneous corpus of knowledge (such as heuristics, physical laws, literature or data) with ML algorithms to create new knowledge, and safe and equitable deployment of technologies with societally beneficial outcomes.

Defining the metric for synthesizability and predicting new compounds that can be experimentally realized in the realm of data-driven research is a pressing problem in contemporary materials science. The increasing computational power and advancements in machine learning (ML) algorithms provide a new avenue to solve the synthesizability challenge. In this work, using the Inorganic Crystal Structure Database (ICSD) and the Materials Project (MP) database, we represent crystal structures in Fourier-transformed crystal properties (FTCP) representation and use a deep learning model to predict synthesizability in the form of a synthesizability score (SC). Such an SC model, as a synthesizability filter for new materials, enables an efficient and accurate classification to identify promising material candidates. The SC prediction model achieved 82.6/80.6% (precision/recall) overall accuracy in predicting ternary crystal materials. We also trained the SC model by only considering compounds uploaded on the MP before 2015 as the training set and testing on multiple sets of materials uploaded after 2015. In the post-2019 test set, we obtain a high 88.60% true positive rate accuracy, coupled with 9.81% precision, indicating that newly added materials remain unexplored and have high synthesis potential. Further, we provide a list of 100 materials predicted to be synthesizable from this post-2019 dataset (highest SC) for future studies, and our SC model, as a validation filter, is beneficial for future material screening and discovery. Agency for Science, Technology and Research (A*STAR) National Research Foundation (NRF) Published version K.H. acknowledges funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043. K.H. also acknowledges funding from the NRF Fellowship NRF-NRFF13-2021-0011. T.B. and Z.R. acknowledge support by the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program through the Singapore−Massachusetts Institute of Technology (MIT) Alliance for Research and Technology’s Low Energy Electronic Systems research program.

With advancements in automated experimental setups, material optimisation and discovery can scale to higher throughput with larger evaluation budgets. Two state-of-the-art algorithms with conceptually different multi-objective optimisation strategies (Bayesian and Evolutionary) are compared on synthetic and real-world datasets. Our results show that the Bayesian optimisation strategy, q-Noisy Expected Hypervolume Improvement (qNEHVI) is superior in finding solutions at the Pareto Front rapidly, and when considering hypervolume improvement as a performance indicator. On the other hand, the Evolutionary optimisation strategy, Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III), can exploit the Pareto Front and propose a larger pool of optimal solutions, given sufficient evaluation budget, and thus may be a better choice for materials discovery problems where knowing the complete Pareto Front provides greater scientific value to understanding materials space. We discuss the limitations of using hypervolume as a performance indicator for optimisation strategies, alongside hypervolume-based strategies such as qNEHVI, which do not adequately explain the number of solutions at or near the Pareto Front. We also performed a comparison of both optimisation strategies at different batch sizes to consider throughput capabilities.

The recent advent of workflows involving high-throughput experimentation techniques, in combination with machine learning optimization, has enabled the accelerated discovery of materials with state-of-the-art properties. However, there remains many other workflows which require measurements of quantities that cannot be easily automated, thereby limiting discovery. In particular, the optimization of the electrical conductivity of doped polymer materials requires laborious measurements. Here, we propose a workflow which involves a data-driven approach using a pair of classification and regression models to reduce the need for manual intervention. The first model classifies the samples at an accuracy of 100% for conductivity >~ 25 to 100 S/cm. We predicted the conductivities of the smaller subset of samples using a second model, which has test R 2 of 0.984. To validate the approach, we showed that the models, neither trained on the samples with the two highest conductivities of 498 and 506 S/cm, were able to, in an extrapolative manner, correctly classify and predict them at errors of 137 and 140 S/cm, respectively. The workflow results in an improvement in measurement efficiency by 59%, which is 89% of the maximum achievable. Concurrently, we addressed the typical lack of explainability of machine-learned models by engineering bespoke features and coupling them with carefully selected models, and derived insights on the spectral influence of conductivity. This study offers a way to accelerate the optimization of doped polymer materials and demonstrates the insights that can be gained with deliberate uses of machine learning in experimental science.

Progress in data-driven self-driving laboratories for solving materials grand challenges has accelerated with the advent of machine learning, robotics, and automation, but they are usually designed with specific materials and processes in mind. To develop the next generation of materials acceleration platforms (MAPs), we propose a unified framework to enable collaboration between MAPs, leveraging on object-oriented programming principles using research groups around theworldthatwouldbeabletoeffectively evolveexperimentalworkflows.Wedemonstratetheframeworkvia three experimental case studies from disparate fields to illustrate theevolutionof,andseamlessintegrationbetween,workflows,promoting efficient resource utilization and collaboration. Moving forward, we project our framework on three other research areas that would benefit from such an evolving workflow. Through the wide adoption of our framework, we envision a collaborative, connected, global community of MAPs working together to solve scientific grand challenges. Agency for Science, Technology and Research (A*STAR) National Research Foundation (NRF) Submitted/Accepted version We acknowledge funding from Accelerated Materials Development for Manufacturing Program A1898b0043 at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research. K.H. also acknowledges funding from the NRF Fellowship NRF-NRFF13-2021- 0011.

The skew and shape of the Molecular Weight Distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we present a computer controlled HTE platform that is able to run up to 8 unique variable conditions for the free radical polymerizations of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time dependent conversion and MWD respectively. In a supervised learning exercise, the gradient-boosted decision trees ML algorithm was used to predict monomer conversion from reagent concentrations and reaction time with high accuracy, using data obtained from the Raman spectra. A second ML algorithm uses the random forest regressor to predict entire MWDs of the synthesized polymer. Each algorithm can navigate the complexity of multiple parallel reactions occurring in a polymerization. The algorithm accurately predicts monomer conversion in the first case despite variations in the polymerization kinetic parameters over time. In the second case, we predict the polymer MWD where fine details such as the shape and skew of the MWD are captured without information loss. SHAP values were calculated to examine the dependence of the ML model output on the experimental conditions. A transfer learning approach was also used to enhance the predictive power of a Deep Neural Network (DNN) model in predicting batch polymerization MWDs. Overall, we demonstrate that the combination of HTE and ML provide a high level of predictive accuracy in determining polymerization outcomes, providing polymer chemists with the ability to target the synthesis of polymers with desired properties via the predicted conversion and MWD.

Thermal rectification is an exotic thermal transport phenomenon which allows heat to transfer in one direction but block the other. We demonstrate an unusual dual-mode solid-state thermal rectification effect using a heterogeneous “irradiated-pristine” polyethylene nanofiber junction as a nanoscale thermal diode, in which heat flow can be rectified in both directions by changing the working temperature. For the nanofiber samples measured here, we observe a maximum thermal rectification factor as large as ~50%, which only requires a small temperature bias of, Here, the authors present a dual-mode solid state thermal rectification effect using a heterogeneous irradiated-pristine polyethylene nanofiber junction as a nanoscale thermal diode, in which heat flow can be rectified in both directions by changing the working temperature.

Local impurity states arising from atomic vacancies in two-dimensional (2D) nanosheets are predicted to have a profound effect on charge transport due to resonant scattering and can be used to manipulate thermoelectric properties. However, the effects of these impurities are often masked by external fluctuations and turbostratic interfaces; therefore, it is challenging to probe the correlation between vacancy impurities and thermoelectric parameters experimentally. In this work, we demonstrate that n-type molybdenum disulfide (MoS2) supported on hexagonal boron nitride (h-BN) substrate reveals a large anomalous positive Seebeck coefficient with strong band hybridization. The presence of vacancies on MoS2 with a large conduction subband splitting of 50.0 ± 5.0 meV may contribute to Kondo insulator-like properties. Furthermore, by tuning the chemical potential, the thermoelectric power factor can be enhanced by up to two orders of magnitude to 50 mW m-1 K-2 Our work shows that defect engineering in 2D materials provides an effective strategy for controlling band structure and tuning thermoelectric transport.

The conceptual understanding of charge transport in conducting polymers is still ambiguous due to a wide range of paracrystallinity (disorder). Here, we advance this understanding by presenting the relationship between transport, electronic density of states and scattering parameter in conducting polymers. We show that the tail of the density of states possesses a Gaussian form confirmed by two-dimensional tight-binding model supported by Density Functional Theory and Molecular Dynamics simulations. Furthermore, by using the Boltzmann Transport Equation, we find that transport can be understood by the scattering parameter and the effective density of states. Our model aligns well with the experimental transport properties of a variety of conducting polymers; the scattering parameter affects electrical conductivity, carrier mobility, and Seebeck coefficient, while the effective density of states only affects the electrical conductivity. We hope our results advance the fundamental understanding of charge transport in conducting polymers to further enhance their performance in electronic applications., Obtaining a complete picture for charge transport in conducting polymers is vital to designing new organic electronic materials. Here, the authors show that a gaussian density of states clarifies the transport physics in conducting polymers by revealing the role of carrier scattering on transport.

Two-dimensional (2D) semiconductors could potentially replace silicon in future electronic devices. However, the low carrier mobility in 2D semiconductors at room temperature, caused by strong phonon scattering, remains a critical challenge. Here we show that lattice distortions can reduce electron–phonon scattering in 2D materials and thus improve the charge carrier mobility. We introduce lattice distortions into 2D molybdenum disulfide (MoS2) using bulged substrates, which create ripples in the 2D material leading to a change in the dielectric constant and a suppressed phonon scattering. A two orders of magnitude enhancement in room-temperature mobility is observed in rippled MoS2, reaching ∼900 cm2 V−1 s−1, which exceeds the predicted phonon-limited mobility of flat MoS2 of 200–410 cm2 V−1 s−1. We show that our approach can be used to create high-performance room-temperature field-effect transistors and thermoelectric devices. Agency for Science, Technology and Research (A*STAR) National Research Foundation (NRF) Submitted/Accepted version J.W. acknowledges the SERC Central Research Fund (CRF KIMR211001kSERCRF) and Advanced Manufacturing and Engineering Young Individual Research Grant (AME YIRG grant no. A2084c170). D.C. and J.W. acknowledge National Research Foundation Competitive Research Programs (NRFCRP24-2020-0002). M.Y. acknowledges funding support (project IDs 1-BE47, ZE0C, ZE2F and ZE2X) from The Hong Kong Polytechnic University. K.H. acknowledges funding from the Accelerated Materials Development for Manufacturing Program at the Agency for Science, Technology and Research (A*STAR) via the AME Programmatic Fund under grant no. A1898b0043. D.X. and T.L. acknowledge the Young Scientist project of the MOE Innovation platform. D.X. acknowledges the National Natural Science Foundation (NSF) of China (grant no. 62104041) and Shanghai Sailing Program (grant no. 21YF1402600). T.L. acknowledges the NSF of Shanghai (grant no. 22ZR1405700).

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). In three design cases, the framework generates 142 new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. These generated crystals, absent in the training database, are validated by first-principles calculations. The success rates (number of first-principles-validated target-satisfying crystals/number of designed crystals) ranges between 7.1% and 38.9%. These results represent a significant step toward property-driven general inverse design using generative models, although practical challenges remain when coupled with experimental synthesis. Agency for Science, Technology and Research (A*STAR) National Research Foundation (NRF) This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program through the Singapore Massachusetts Institute of Technology (MIT) Alliance for Research and Technology’s Low Energy Electronic Systems (LEES) research program. F.O., S.S., and Q. Liang acknowledge support from Total Energies Research grant funded through MITei. Y.J. acknowledges the support from the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2021-0-02068, Artificial Intelligence Innovation Hub). J.L. and X.W. acknowledge support from the Ministry of Education Academic Research Fund R-279-000-532-114,. Y.L. is supported by the National Key Research and Development Program of China (Grant Nos. 2017YFB0702901 and 2017YFB0701502) and the National Natural Science Foundation of China (Grant No. 91641128). S.J. and K.H. acknowledge funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043. G.X. is grateful for the support by the Scientific Computing and Data Analysis section of the Research Support Division at Okinawa Institute of Science and Technology Graduate University (OIST). Q.Li is supported by the National Research Foundation (NRF) fellowship grant NRFF13-2021-0106. A.G.A. acknowledges support from Solar Energy Research Institute of Singapore (SERIS). SERIS is a research institute at the National University of Singapore (NUS). SERIS is supported by the National University of Singapore (NUS), the National Research Foundation Singapore (NRF), the Energy Market Authority of Singapore (EMA), and the Singapore Economic Development Board (EDB).

With advancements in automation and high-throughput techniques, complex materials discovery with multiple conflicting objectives can now be tackled in experimental labs. Given that physical experimentation is greatly limited by evaluation budget, maximizing efficiency of optimization becomes crucial. We discuss the limitations of using hypervolume as a performance indicator for desired optimality across the entire multi-objective optimization run and propose new metrics specific to experimentation: ability to perform well for complex high-dimensional problems, minimizing wastage of evaluations, consistency/robustness of optimization, and ability to scale well to high throughputs. With these metrics, we perform a comparison of two conceptually different and state-of-the-art algorithms (Bayesian and Evolutionary) on synthetic and real-world datasets. We discuss the merits of both approaches with respect to exploration and exploitation, where fully resolving the Pareto Front could be the main aim for greater scientific value in understanding materials space, and thus provide a perspective for materials scientists to implement optimization in their platforms.

Solar water splitting offers a potential avenue for the production of clean and storable energy in the form of hydrogen. Semiconductors can be used as photocatalysts that enable the simultaneous production of hydrogen and oxygen from water via water splitting and in recent years inorganic semiconductor photocatalysts have been significantly improved in terms of their performance with organic semiconductors emerging as a potential alternative, though mostly studied for sacrificial half-reactions. Herein, we present recent developments in using organic semiconductors as photocatalysts highlighting their potential due to their synthetic tunability. We will particularly focus on their application in overall water splitting without using sacrificial reagents and suggest future directions in using these materials in large scale applications before concluding with suggestions for a wider community to focus research efforts on particular challenges in the field and opportunities that organic materials offer.

The past few decades have seen an uptick in the scope and range of device applications of organic semiconductors, such as organic field-effect transistors, organic photovoltaics and light-emitting diodes. Several researchers have studied electrical transport in these materials and proposed physical models to describe charge transport with different material parameters, with most disordered semiconductors exhibiting hopping transport. However, there exists a lack of a consensus among the different models to describe hopping transport accurately and uniformly. In this work, we first evaluate the efficacy of using a purely data-driven approach, i.e., symbolic regression, in unravelling the relationship between the measured field-effect mobility and the controllable inputs of temperature and gate voltage. While the regressor is able to capture the scaled mobility well with mean absolute error (MAE) ~O(10-2), better than the traditionally used hopping transport model, it is unable to derive physically interpretable input-output relationships. We then examine a physics-inspired renormalization approach to describe the scaled mobility with respect to a scale-invariant reference temperature. We observe that the renormalization approach offers more generality and interpretability with a MAE of the ~O(10-1), still better than the traditionally used hopping model, but less accurate as compared to the symbolic regression approach. Our work shows that physics-based approaches are powerful compared to purely data-driven modelling, providing an intuitive understanding of data with extrapolative ability.

Thermoelectric materials have the ability to convert heat energy to electrical power and vice versa. While the thermodynamic upper limit is defined by the Carnot efficiency, the material figure of merit, zT, is far from this theoretical limit, typically limited by a complex interplay of non-equilibrium charge and phonon-scattering. Materials innovation is a slow, arduous process due to the complex correlations between crystal structure, microstructure engineering, and thermoelectric properties. Many physical concepts and materials have been unearthed in this path to discovery, supported ably by innovations in technology over many decades, revealing important material and transport descriptors. In this review, we look back at some case studies of inorganic thermoelectric materials employing a bird’s-eye view of complementary advancements in scientific concepts and technological advancements and conclude that most high values of zT have emerged from developed scientific models fueled by moderately mature technologies. On the basis of this conclusion, we then propose that the recent emergence of data-driven approaches and high-throughput experiments, encompassing synthesis as well as characterization, with machine learning guided inverse design is perfectly suited to provide an accelerated pathway toward the discovery of next-generation thermoelectric materials, potentially providing a feasible alternative source of energy for a sustainable future.